17 citations · 31 across the 9 of their papers we have counts for
11 papers
Rethinking Soft Label in Label Distribution Learning Perspective
Seungbum Hong, Jihun Yoon, Bogyu Park +1
The primary goal of training in early convolutional neural networks (CNN) is the higher generalization performance of the model. However, as the expected calibration error (ECE), w…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Blockwise Temporal-Spatial Pathway Network
SeulGi Hong, Min-Kook Choi
Algorithms for video action recognition should consider not only spatial information but also temporal relations, which remains challenging. We propose a 3D-CNN-based action recogn…
hSDB-instrument: Instrument Localization Database for Laparoscopic and Robotic Surgeries
Jihun Yoon, Jiwon Lee, Sunghwan Heo +9
Automated surgical instrument localization is an important technology to understand the surgical process and in order to analyze them to provide meaningful guidance during surgery…
Self-Supervised Knowledge Transfer via Loosely Supervised Auxiliary Tasks
Seungbum Hong, Jihun Yoon, Junmo Kim +1
Knowledge transfer using convolutional neural networks (CNNs) can help efficiently train a CNN with fewer parameters or maximize the generalization performance under limited superv…
Rethinking Generalization Performance of Surgical Phase Recognition with Expert-Generated Annotations
Seungbum Hong, Jiwon Lee, Bokyung Park +5
As the area of application of deep neural networks expands to areas requiring expertise, e.g., in medicine and law, more exquisite annotation processes for expert knowledge trainin…
Deep Active Learning with Augmentation-based Consistency Estimation
SeulGi Hong, Heonjin Ha, Junmo Kim +1
In active learning, the focus is mainly on the selection strategy of unlabeled data for enhancing the generalization capability of the next learning cycle. For this, various uncert…
Semi-Supervised Object Detection with Sparsely Annotated Dataset
Jihun Yoon, Seungbum Hong, Sanha Jeong +1
In training object detector based on convolutional neural networks, selection of effective positive examples for training is an important factor. However, when training an anchor-b…
Development of Fast Refinement Detectors on AI Edge Platforms
Min-Kook Choi, Heechul Jung
With the improvements in the object detection networks, several variations of object detection networks have been achieved impressive performance. However, the performance evaluati…
Co-occurrence matrix analysis-based semi-supervised training for object detection
Min-Kook Choi, Jaehyeong Park, Jihun Jung +6
One of the most important factors in training object recognition networks using convolutional neural networks (CNNs) is the provision of annotated data accompanying human judgment.…
End-to-End Pedestrian Collision Warning System based on a Convolutional Neural Network with Semantic Segmentation
Heechul Jung, Min-Kook Choi, Kwon Soon +1
Traditional pedestrian collision warning systems sometimes raise alarms even when there is no danger (e.g., when all pedestrians are walking on the sidewalk). These false alarms ca…